Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Daly, A; Hess, S; Dekker, T (2014)
Publisher: National Academy of Sciences
Languages: English
Types: Article
Many large-scale real-world transport applications have choice sets that are so large as to make model estimation and application computationally impractical. The ability to estimate models on subsets of the alternatives is thus of great appeal, and correction approaches have existed since the late 1970s for the simple multinomial logit (MNL) model. However, many of these models in practice rely on nested logit specifications, for example, in the context of the joint choice of mode and destination. Recent research has put forward solutions for such generalized extreme value (GEV) structures, but these structures remain difficult to apply in practice. This paper puts forward a simplification of the GEV method for use in computationally efficient implementations of nested logit. The good performance of this approach is illustrated with simulated data, and additional insights into sampling error are also provided with different sampling strategies for MNL.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Bradley, Mark, John L. Bowman and Bruce Griesenbeck (2010) SACSIM: An applied activitybased model system with fine-level spatial and temporal resolution, Journal of Choice Modelling 3(1), pp. 5-31.
    • [2] McFadden, D.L. (1978), Modelling the choice of residential location, in Karlqvist, A., Lundqvist, L., Snickars, F. and Weibull, J., Spatial interaction theory and residential location, North-Holland, pp. 75-96.
    • [3] Guevara, C.A. and Ben-Akiva, M. (2013), Sampling of alternatives in multivariate extreme value (MEV) models, Transportation Research Part B 48, pp. 31 52
    • [4] Nerella, S. and Bhat, C. (2004), A numerical analysis of the effect of sampling of alternatives in discrete choice models, TRB.
    • [5] Ben-Akiva, M. and Lerman, S. (1985), Discrete Choice Analysis: theory and application to travel demand, MIT Press, see pp. 261-269 (Estimation of choice models with a sample of alternatives).
    • [6] Miller, S., Daly, A., Fox, J. and Kohli, S. (2007), Destination sampling in forecasting: application in the PRISM model for the UK West Midlands Region, presented to European Transport Conference, Noordwijkerhout.
    • [7] Hammersley, J. and Handscomb, D. (1964), Monte Carlo Methods, Chapman and Hall, pp. 57- 59 (Importance Sampling).
    • [8] Koppelman, F. and Garrow, L. (2005), Efficiently estimating nested logit models with choicebased samples: Example applications, Transportation Research Record 1921: 63-69.
    • [9] Mabit, S. and Fosgerau, M. (2006) unpublished note, Danish Technical University (extract from
    • [10] Bierlaire, M., Bolduc, D. and McFadden, D. (2008), The estimation of generalized extreme value models from choice-based samples, Trans. Res. B, 42, pp. 381-394.
    • [11] Frejinger, E., Bierlaire, M. and Ben-Akiva, M. (2009), Sampling of alternatives for route choice modelling, Trans. Res. B, 43, pp. 984-994.
    • [12] Train, K. (2009), Discrete Choice Methods with Simulation, second edition, Cambridge University Press, Cambridge, MA.
    • [13] Lee, B.H. and Waddell, P (2010), Residential mobility and location choice: a nested logit model with sampling of alternatives, Transportation, 37, pp. 587-601.
    • [15] Hensher, D.A., Rose, J.M. & Greene, W.H. (2005), Applied Choice Analysis: A Primer, Cambridge University Press, Cambridge, MA.
    • Table 6: Estimation results from simulated mode (M) - destination (D) case studies Setting 1 Setting 2 Setting 3 Setting 4 ttc -0.03 -0.05 -0.07 -0.09 ttPT -0.07 -0.12 -0.16 -0.21
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article